no code implementations • 29 Apr 2024 • Han Zhong, Guhao Feng, Wei Xiong, Li Zhao, Di He, Jiang Bian, LiWei Wang
For its practical implementation, \texttt{RTO} innovatively integrates Direct Preference Optimization (DPO) and PPO.
no code implementations • 21 Feb 2024 • Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, LiWei Wang
Our results show that while these models are expressive enough to solve general DP tasks, contrary to expectations, they require a model size that scales with the problem size.
no code implementations • 29 Jan 2024 • Zhenyu He, Guhao Feng, Shengjie Luo, Kai Yang, Di He, Jingjing Xu, Zhi Zhang, Hongxia Yang, LiWei Wang
In this work, we leverage the intrinsic segmentation of language sequences and design a new positional encoding method called Bilevel Positional Encoding (BiPE).
no code implementations • 28 Dec 2023 • Guhao Feng, Han Zhong
We first demonstrate that, for a broad class of Markov decision processes (MDPs), the model can be represented by constant-depth circuits with polynomial size or Multi-Layer Perceptrons (MLPs) with constant layers and polynomial hidden dimension.
no code implementations • NeurIPS 2023 • Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, LiWei Wang
By using circuit complexity theory, we first give impossibility results showing that bounded-depth Transformers are unable to directly produce correct answers for basic arithmetic/equation tasks unless the model size grows super-polynomially with respect to the input length.
1 code implementation • 14 Feb 2023 • Bohang Zhang, Guhao Feng, Yiheng Du, Di He, LiWei Wang
Recently, subgraph GNNs have emerged as an important direction for developing expressive graph neural networks (GNNs).
Ranked #1 on Subgraph Counting - C6 on Synthetic Graph